Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks

TL;DR

This paper presents an attention-based transformer neural network that accurately reconstructs Richtmyer-Meshkov instabilities from noisy radiographic images, improving robustness against noise and image corruption.

Contribution

The study introduces a novel transformer encoder architecture that effectively extracts and utilizes low-dimensional features from noisy radiographs to recover complex hydrodynamic instabilities.

Findings

Accurately recovers instability growth rates despite noise

Demonstrates robustness on simulated ICF-like data

Uses self-attention to learn temporal dependencies

Abstract

A trained attention-based transformer network can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. This approach is demonstrated on ICF-like double shell hydrodynamic simulations. The key component of this network is a transformer encoder that acts on a sequence of features extracted from noisy radiographs. This encoder includes numerous self-attention layers that act to learn temporal dependencies in the input sequences and increase the expressiveness of the model. This approach is demonstrated to exhibit an excellent ability to accurately recover the Richtmyer-Meshkov instability growth rates, even despite the gas-metal interface being greatly obscured by radiographic noise.